In recent years, Unmanned Aerial Vehicles (UAVs) have been widely utilized across many fields, but their limited computing resources and battery capacity make it difficult for them to process computation-intensive tasks locally. The development of Mobile Edge Computing (MEC) enables UAVs connected via cellular networks to offload tasks to ground base stations equipped with MEC servers. To process heterogeneous tasks on the servers, the corresponding services, such as programs, libraries, and databases, should be placed. In this paper, we formulate a Mixed-Integer NonLinear Programming (MINLP) problem in the time-varying multi-UAV multi-MEC server system, focusing on jointly optimizing service placement, task assignment, and transmission power allocation to minimize the system cost (weighted sum of consumed time and energy). To effectively address this optimization problem, we model it as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL) based approach for online decision-making. Simulation experiments show that the proposed approach can converge quickly and stably, and more effectively reduce the system cost compared to other baseline schemes.

Block-based visual programming languages enable programmers to focus on programming thinking without excessive coding or syntax attention. We find that projects written in visual programming languages like Scratch also have bugs and code smells that cannot be avoided automatically. Existing studies analyze these problems by converting code into Abstract Syntax Trees (ASTs) before analysis. This process is time-consuming and analyzing the AST directly can be complex. In this paper, we propose a novel code image-based static analysis approach for visual programming languages, which mines code patterns in images to pre-determine whether there are problems in projects without parsing them first. We train a code image object detection network to judge whether the input projects are likely to have bugs or smells. Further analysis is performed only on potentially problematic projects. Based on the proposals of the detection network, we analyze the key areas of the AST and identify the specific code problems. Experiments show that compared with others, our method can quickly determine code problems and accurately locate them. For the code image detection task, the improved model can achieve higher mean average precision (MAP) faster in the training phase, while the model is smaller and faster in inference.

As one of the most popular visual programming languages, Scratch has a lot of evaluation around it. Reasonable evaluation can help programmers understand their projects better. At the same time, it can also provide a reference for them to browse other projects in the online community. Most of the existing evaluations on Scratch are carried from three perspectives: Computational Thinking (CT) ability, visual presentation aesthetics, and code quality. Among them, the assessment of CT and code quality is mainly carried out from the program script, while the evaluation of visual aesthetics is analyzed from the perspective of image sequences generated by project execution. The single-view evaluation focuses on the performance of a program in a certain aspect and is one-sided. In this paper, we propose a multi-view evaluation framework to integrate various evaluations using different policies. We quantitatively analyze the assessment of different views driven by data. Combined with overall evaluations that represent human opinions, we analyze their differences and connections. Through experiments, we determine the weights of different integration policies, the proposed multi-view evaluation method can generate evaluation results similar to human opinions.

As the smart grid develops rapidly, abundant connected devices offer various trading data. This raises higher requirements for secure and effective data storage. Traditional centralized data management does not meet the above requirements. Currently, smart grid with conventional consortium blockchain can solve the above issues. However, in the face of a large number of nodes, existing consensus algorithms often perform poorly in terms of efficiency and throughput. In this paper, we propose a trust-based hierarchical consensus mechanism (THCM) to solve this problem. Firstly, we design a hierarchical mechanism to improve the efficiency and throughput. Then, intra-layer nodes use an improved Raft consensus algorithm and inter-layer nodes use the Byzantine Fault Tolerance algorithm. Thirdly, we propose a trust evaluation method to improve the election process of Raft. Finally, we implement a prototype system to evaluate the performance of THCM. The results demonstrate that the consensus efficiency is improved by 19.8%, the throughput is improved by 12.34%, and the storage is reduced by 37.9%.

In this paper, we target a similarity search among data supply chains, which plays an essential role in optimizing the supply chain and extending its value. This problem is very challenging for application-oriented data supply chains because the high complexity of the data supply chain makes the computation of similarity extremely complex and inefficient. In this paper, we propose a feature space representation model based on key points, which can extract the key features from the subsequences of the original data supply chain and simplify it into a feature vector form. Then, we formulate the similarity computation of the subsequences based on the multiscale features. Further, we propose an improved hierarchical clustering algorithm for a similarity search over the data supply chains. The main idea is to separate the subsequences into disjoint groups such that each group meets one specific clustering criteria; thus, the cluster containing the query object is the similarity search result. The experimental results show that the proposed approach is both effective and efficient for data supply chain retrieval.